Owais Ahmed Malik

Software level green computing for large scale systems

Energy conservation has become a critical issue in modern system electronic devices. Energy wastage in electronic devices occurs in both hardware and software components. Software drives the hardware thus decisions taken during software design and development have significant impact on energy consumption of a computing system. Green Computing addresses energy conservation by application of different techniques at software and hardware level. Energy efficient compiler is a software level green computing technique. Besides compiler optimization, an effective scheduling approach makes efficient use of resources to directly impact the green aspect. Therefore, focus of this paper is identification of energy conservation measures for software level and there utilization at compiler and scheduler. A Distributed Green Compiler (DGC) is presented in this research that is hardware independent and uses an existing distributed compiler. It distributes source code of software over a network, reshapes binary code by applying green strategies during code transformation at compile time and gives green suggestion to software programmer for energy conservation. For scheduling, Distributed Interactive Engineering Toolbox (DIET) scheduler is used and a new algorithm is proposed for the DIET scheduler. The proposed algorithm introduces green aspect in scheduler to effectually make use of resources in such a way that consumption of power and carbon dioxide emission is reduced. Performance analysis of proposed compiler shows that it conserves energy clock cycles up to 40% by applying few green strategies.

An Intelligent Recovery Progress Evaluation System for ACL Reconstructed Subjects Using Integrated 3-D Kinematics and EMG Features

An intelligent recovery evaluation system is presented for objective assessment and performance monitoring of anterior cruciate ligament reconstructed (ACL-R) subjects. The system acquires 3-D kinematics of tibiofemoral joint and electromyography (EMG) data from surrounding muscles during various ambulatory and balance testing activities through wireless body-mounted inertial and EMG sensors, respectively. An integrated feature set is generated based on different features extracted from data collected for each activity. The fuzzy clustering and adaptive neuro-fuzzy inference techniques are applied to these integrated feature sets in order to provide different recovery progress assessment indicators (e.g., current stage of recovery, percentage of recovery progress as compared to healthy group, etc.) for ACL-R subjects. The system was trained and tested on data collected from a group of healthy and ACL-R subjects. For recovery stage identification, the average testing accuracy of the system was found above 95% (95-99%) for ambulatory activities and above 80% (80-84%) for balance testing activities. The overall recovery evaluation performed by the proposed system was found consistent with the assessment made by the physiotherapists using standard subjective/objective scores. The validated system can potentially be used as a decision supporting tool by physiatrists, physiotherapists, and clinicians for quantitative rehabilitation analysis of ACL-R subjects in conjunction with the existing recovery monitoring systems.

A knowledge-based intelligent framework for anterior cruciate ligament rehabilitation monitoring

Abstract This study presents an integration of knowledge-based system and intelligent methods to develop a recovery monitoring framework for post anterior cruciate ligament (ACL) injured/reconstructed subjects. The case based reasoning methodology has been combined with fuzzy clustering and intelligent classification techniques in order to develop a knowledge base and a learning model for identifying the recovery stage of ACL-reconstructed subjects and objectively monitoring the progress during the convalescence regimen. The system records kinematics and neuromuscular signals from lower limbs of healthy and ACL-reconstructed subjects using self adjusted non-invasive body-mounted wireless sensors. These bio-signals are synchronized and integrated, and a combined feature set is generated by performing data transformation using wavelet decomposition and feature reduction techniques. The knowledge base stores the subjects’ profiles, their recovery sessions’ data and problem/solution pairs for different activities monitored during the course of rehabilitation. Fuzzy clustering technique has been employed to form the initial groups of subjects at similar stage of recovery. In order to classify the recovery stage of subjects (i.e. retrieval of similar cases), adaptive neuro-fuzzy inference system ( ANFIS ), fuzzy unordered rule induction algorithm ( FURIA ) and support vector machine ( SVM ) have been applied and compared. The system has been successfully tested on a group of healthy and post-operated athletes for analyzing their performance in two activities (ambulation at various speeds and one leg balance testing) selected from the rehabilitation protocol. The case adaptation and retention is a semi-automatic process requiring input from the physiotherapists and physiatrists. This intelligent framework can be utilized by physiatrists, physiotherapists, sports trainers and clinicians for multiple purposes including maintaining athletes’ profile, monitoring progress of recovery, classifying recovery status, adapting recovery protocols and predicting/comparing athletes’ sports performance. Further, the knowledge base can easily be extended and enhanced for monitoring different types of sports activities.

Wireless Multi-Sensor Integration for ACL Rehabilitation Using Biofeedback Mechanism

The objective of this study is to propose an integrated motion analysis system for monitoring and assisting the rehabilitation process for athletes based on biofeedback mechanism, particularly for human subjects already undergone Anterior Cruciate Ligament (ACL) injury operations and thus about to start the rehabilitation process. For this purpose, different types of parameters (kinematics and neuromuscular signals) from multi-sensors integration are combined to analyze the motion of affected athletes. Signals acquired from sensors are pre-processed in order to prepare the pattern set for intelligent algorithms to be integrated for possible implementation of effective assistive rehabilitation processing tools for athletes and sports orthopedic surgeons. Based on the characteristics of different signals invoked during the rehabilitation process, two different intelligent approaches (Elman RNN and Fuzzy Logic) have been tested. The newly introduced integrated multi-sensors approach will assist in identifying the clinical stage of the recovery process of athletes after ACL repair and will facilitate clinical decision-making during the rehabilitation process. The use of wearable wireless miniature sensors will provide an un-obstructive assessment of the kinematics and neuromuscular changes occurring after ACL reconstruction in an athlete.Copyright

Reducing user latency in web prefetching using integrated techniques

Web caching and Web Prefetching are the areas for the research in Web Mining. Web Prefetching improves the performance of the Web Caching techniques due to prediction of the user pages in advance before the user requests. Both techniques provide the web pages local to the user; they provide the resources of web for users ease and access. Web caching is limited due to its size. Web prefetching is the process of accessing the web objects before the users request. Whenever a client requests before accessing the web page a prediction is made for accessing that web page. All the web objects are brought from server to the client. The access to the web objects are on the basis of the data prefetched from the server. This research focused on when a user requests for a web page, how to improve the overall performance of web prefetching mechanism? The proposed mechanism provides the pages locally available to a user or group of users by utilizing bandwidth of the network. The server contains an algorithm for the prediction of web pages and the prediction of a web page is based on counting the number of times a page is accessed by a user from each cluster.

Artificial neural network based gait patterns identification using neuromuscular signals and soft tissue deformation analysis of lower limbs muscles

The objective of this study is to investigate the use of electromyography (EMG) signals and video based soft tissue deformation (STD) analysis for identifying the gait patterns of healthy and injured subjects. The system includes a wireless surface electromyography (EMG) sensor unit and two video camera systems for measuring the neuromuscular activity of lower limb muscles, and a custom-developed artificial neural network based intelligent system software for identifying the gait patterns of subjects during walking activity. The system uses root mean square (RMS) value of EMG signals and soft tissue deformation parameter (STDP) as the input features. In order to estimate the STD during a muscular contraction while walking, flexible triangular meshes are built on reference points. The positions of these selected points are evaluated by applying the block matching motion estimation technique. Based on the extracted features, multilayer feed-forward backpropagation networks (FFBPNNs) with different network training functions were designed and their classification performances were compared. The system has been tested for a group of healthy and injured subjects. The results showed that FFBPNN with Levenberg-Marquardt training function provided better prediction behavior (98% overall accuracy) as compared to FFBPNN with other training functions for gait patterns identification based on RMS value of EMG and STDP.

CBR and neural networks based technique for predictive prefetching

Cache prefetching in memory management greatly relies upon effectiveness of prediction mechanism to fully exploit available resources and for avoiding page faults. Plenty of techniques are available to devise strong prediction mechanism for prefetching but they either are situation specific (Locality of reference principle) or inadaptable (Markovian model) and costly. We have proposed a generic and adaptable technique benefiting from past experience by employing hybrid of Case Based Reasoning (CBR) and Neural Networks (NNs). Here we will be concerned with improving adaptation phase of CBR using NN and its impact on predictive accuracy for prefetching. The level of predictive accuracy attained (specifically in case adaptation of CBR) is ameliorated by handsome margin with declined cost than contemporary techniques as would be affirmed by results.

A hybrid intelligent system to improve predictive accuracy for cache prefetching

Cache being the fastest medium in memory hierarchy has a vital role to play for fully exploiting available resources, concealing latencies in IO operations, languishing the impact of these latencies and hence in improving system response time. Despite plenty of efforts made, caches alone cannot comprehend larger storage requirements without prefetching. Cache prefetching is speculatively fetching data to restrain all delays. However, effective prefetching requires a strong prediction mechanism to load relevant data with higher degree of accuracy. In order to ameliorate the predictive performance of cache prefetching, we applied the hybrid of two AI approaches named case based reasoning (CBR) and artificial neural networks (ANN). CBR maintains the past experience and ANN are used in adaptation phase of CBR instead of employing static rule base. The novelty of technique in this domain is valued due to hybrid of two approaches as well as usage of suffix tree in populating the CBRs case base. Suffix trees provide rich data patterns for populating case base and greatly enhanced the overall performance. A number of evaluations from different aspects with varying parameters are presented (along with some findings) where the efficacy of our technique is affirmed with improved predictive accuracy and reduced level of associated costs.

An Interval Type-2 Fuzzy Logic Based Classification Model for Testing Single-Leg Balance Performance of Athletes after Knee Surgery

Single-leg balance test is one of the most common assessment methods in order to evaluate the athletes’ ability to perform certain sports actions efficiently, quickly and safely. The balance and postural control of an athlete is usually affected after a lower limb injury. This study proposes an interval type-2 fuzzy logic (FL) based automated classification model for single-leg balance assessment of subjects after knee surgery. The system uses the integrated kinematics and electromyography (EMG) data from the weight-bearing leg during the balance test in order to classify the performance of a subject. The data are recorded through wearable wireless motion and EMG sensors. The parameters for the membership functions of input and output features are determined using the data recorded from a group of athletes (healthy/having knee surgery) and the recommendations from physiotherapists and physiatrists, respectively. Four types of fuzzy logic systems namely type-1 non-singleton interval type-2 (NSFLS type-2), singleton type-2 (SFLS type-2), non-singleton type-1 (NSFLS type-1) and singleton type-1 (SFLS type-1) were designed and their performances were compared. The overall classification accuracy results show that the interval type-2 FL system outperforms the type-1 FL system in classifying the balance test performance of the subjects. This pilot study suggests that a fuzzy logic based automated model can be developed in order to facilitate the physiotherapists and physiatrists in determining the impairments in the balance control of the athletes after knee surgery.

A Real-Time Intelligent Biofeedback Gait Patterns Analysis System for Knee Injured Subjects

This study presents a real-time visualization system of gait patterns of knee injured subjects for biofeedback monitoring and classification. The developed system includes non-invasive wireless body-mounted motion sensors for kinematics measurements of lower extremities, surface electromyography (EMG) system for relevant specific muscle activity measurements, a motion capture system for recording trial activities and custom-developed intelligent system software implemented using LabVIEW and MATLAB. The real-time biofeedback system provides a visual monitoring of individual and superimposed signals (kinematics, EMG and video data) in order to identify the knee joint abnormality and muscles strength during various ambulation activities performed by the subjects. It can facilitate the clinicians, physiotherapists and physiatrists in determining the impairments in the gait patterns the knee injured based on the data collected and identifying the subjects lacking behind the desired level of recuperation.